class_docstr: Fill in missing each missing row/column value by averaging across the
  k-nearest neighbors columns (taking into account missing data when
  computing column similarities and choosing which neighbors to inspect).
  
  Currently does not inherit from Solver since it expects sparse inputs in
  the form of nested dictionaries.
docstr: Parameters
  ----------
  min_weight_for_similarity : float
      If sum of values in shared rows between two columns falls below this
      threhold then similarity can't be computed between those columns.
  
  min_count_for_similarity : int
      If number of overlapping rows between two columns falls below this
      threhold then similarity can't be computed between those columns.
  
  similarity_exponent : float
      Exponent for turning similarities into weights on values of other
      columns.
  
  shrinkage_value : float
      Shrinks reconstructed values toward 0
  
  orientation : str
      Whether to compute similarities along rows or columns
  
  verbose : bool
docstr_tok: ["Parameters","--","--","--","--","--","min_weight_for_similarity",":","float","If","sum","of","values","in","shared","rows","between","two","columns","falls","below","this","threhold","then","similarity","ca","n't","be","computed","between","those","columns",".","min_count_for_similarity",":","int","If","number","of","overlapping","rows","between","two","columns","falls","below","this","threhold","then","similarity","ca","n't","be","computed","between","those","columns",".","similarity_exponent",":","float","Exponent","for","turning","similarities","into","weights","on","values","of","other","columns",".","shrinkage_value",":","float","Shrinks","reconstructed","values","toward","0","orientation",":","str","Whether","to","compute","similarities","along","rows","or","columns","verbose",":","bool"]
code: def __init__(
      self,
      min_weight_for_similarity=0.1,
      min_count_for_similarity=2,
      similarity_exponent=4.0,
      shrinkage_value=0.0001,
      orientation="rows",
      verbose=False,
  ):
      """
      Parameters
      ----------
      min_weight_for_similarity : float
          If sum of values in shared rows between two columns falls below this
          threhold then similarity can't be computed between those columns.
  
      min_count_for_similarity : int
          If number of overlapping rows between two columns falls below this
          threhold then similarity can't be computed between those columns.
  
      similarity_exponent : float
          Exponent for turning similarities into weights on values of other
          columns.
  
      shrinkage_value : float
          Shrinks reconstructed values toward 0
  
      orientation : str
          Whether to compute similarities along rows or columns
  
      verbose : bool
      """
      self.min_weight_for_similarity = min_weight_for_similarity
      self.min_count_for_similarity = min_count_for_similarity
      self.similarity_exponent = similarity_exponent
      self.shrinkage_value = shrinkage_value
      self.orientation = orientation
      self.verbose = verbose
  
file_path: fancyimpute/fancyimpute/similarity_weighted_averaging.py
imports: from collections import defaultdict
  from sklearn.utils import check_array
  import numpy
  from .dictionary_helpers import collect_nested_keys, reverse_lookup_from_nested_dict, matrix_to_nested_dictionary, transpose_nested_dictionary
label_desc: Use image node_id 1 to create a new SimilarityWeightedAveraging object from inherited base classes: object with example: obj = SimilarityWeightedAveraging(min_weight_for_similarity, min_count_for_similarity, similarity_exponent, shrinkage_value, orientation, verbose)